A personal route prediction system based on trajectory data mining

This paper presents a system where the personal route of a user is predicted using a probabilistic model built from the historical trajectory data. Route patterns are extracted from personal trajectory data using a novel mining algorithm, Continuous Route Pattern Mining (CRPM), which can tolerate different kinds of disturbance in trajectory data. Furthermore, a client–server architecture is employed which has the dual purpose of guaranteeing the privacy of personal data and greatly reducing the computational load on mobile devices. An evaluation using a corpus of trajectory data from 17 people demonstrates that CRPM can extract longer route patterns than current methods. Moreover, the average correct rate of one step prediction of our system is greater than 71%, and the average Levenshtein distance of continuous route prediction of our system is about 30% shorter than that of the Markov model based method

Optimal-Location-Selection Query Processing in Spatial Databases

Abstract—This paper introduces and solves a novel type of spatial queries, namely, Optimal-Location-Selection (OLS) search, which has many applications in real life. Given a data object set DA, a target object set DB, a spatial region R, and a critical distance dc in a multidimensional space, an OLS query retrieves those target objects in DB that are outside R but have maximal optimality. Here, the optimality of a target object b 2 DB located outside R is defined as the number of the data objects from DA that are inside R and meanwhile have their distances to b not exceeding dc. When there is a tie, the accumulated distance from the data objects to b serves as the tie breaker, and the one with smaller distance has the better optimality. In this paper, we present the optimality metric, formalize the OLS query, and propose several algorithms for processing OLS queries efficiently. A comprehensive experimental evaluation has been conducted using both real and synthetic data sets to demonstrate the efficiency and effectiveness of the proposed algorithms. Index Terms—Query processing, optimal-location-selection, spatial database, algorithm. Ç

Detecting Traffic Congestions Using Cell Phone Accelerometers

Abstract In this paper, we propose a system that detects traffic congestions by using cell phone accelerometers, which have many advantages (e.g. energy-efficient, unobtrusive, impervious to environmental noise, etc.). However, it is challenging to extract well-targeted and accurate features (e.g. speed) for detecting traffic congestions in a complex daily-living environment using a single cell phone accelerometer. The proposed system comprises a vehicular movement detection module, and a module for likelihood estimation of traffic congestions. Experimental results based on real datasets have demonstrated the effectiveness of the proposed system

Microglia and astrocytoma cells express alkylindole-sensitive receptors

Thesis (Ph.D.)--University of Washington, 2014Two cannabinoid (CB) receptors, CB1 and CB2, have been identified at the molecular level. Evidence suggests that other cannabinoid and cannabinboid-like receptors remain to be identified. Using newly developed compounds, our lab has identified receptors sensitive to alkylindoles (AI) which exhibit a distinct pharmacological profile from CB1 and CB2 receptors and are expressed by both microglia and astrocytomas. Radioligand binding data show that WIN-2, and novel analogues, ST-11 and ST-48, compete for [3H]WIN-2 binding in DBT mouse astrocytoma cells and primary microglia in culture, while classic cannabinoid ligands did not. In microglia, ST-11 and ST-48 increase intracellular cAMP levels, suggesting that AI receptors are GPCRs that couple to Gs. As resident immune cells of the CNS, microglia play many roles in maintaining homeostasis in a healthy brain. AI analogues inhibit both basal and ATP-stimulated microglia cell migration. The compounds did not affect cell viability, yet they did inhibit cell proliferation stimulated by macrophage colony stimulating factor (M-CSF). Interestingly, AI analogues did not modulate effector proteins that characterize M1 or M2 phenotypes. AI analogues also inhibited DBT cell migration, though with lower efficacy. In contrast to microglia, AI analogues did reduce cell viability and basal cell proliferation, suggesting that AI-sensitive receptors expressed by cancer cells may be an effective target to reduce tumor growth. In vivo study of ST-11 formulated in liposomes show that tumor volume was reduced while microglia reactivity was increased. Further study of AI analogues is needed to increase understanding of this phenomenon